An accelerated CLPSO algorithm (original) (raw)

Particle Swarm Optimization: Methods, Taxonomy and Applications

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s, and since then has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. In this paper, following a brief introduction to the PSO algorithm, the chronology of its evolution is presented and all major PSO-based methods are comprehensively surveyed. Next, these methods are studied separately and their important factors and parameters are summarized in a comparative table. In addition, a new taxonomy of PSO-based methods is presented. It is the purpose of this paper to present an overview of the previous and present status of PSO algorithms well as its opportunities and challenges. Accordingly, the history, various methods, and taxonomy of this algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

Particle Swarm Optimization -A Tutorial

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

Particle swarm optimization: performance tuning and empirical analysis

2009

This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.

LDWMeanPSO: A new improved particle swarm optimization technique

Computer Engineering …, 2011

Abstract Different optimization functions are used to develop the particle swarm optimization (PSO) technique, based on natural and physical phenomena. The presented techniques range from Standard PSO, Linearly Decreasing Weight PSO, Center PSO, Mean PSO and ...